Existing data mining systems and methods such as decision trees, multidimensional data sources, and data mining work flows have several limitations.
In particular, decision trees are used for analysis of data structures to reveal relationships and patterns the purpose of which is to apply analytical techniques and statistical methods to reveal these relationships and patterns—expressed as models or scores. Analytical techniques and statistical methods consist for example of segmentation, classification, and estimation. There is generally a requirement for a dependent or target variable which defines the shape of the tree in commercial applications. Recent enhancements relate mostly to the improved performance of trees or their ability to create outputs for use in developing predictive models. Data analysis, as well as creation and deployment of rules, are typically limited by this construct/framework. For example, the amount of time required to create decision tables is extensive. In addition, multidimensional data sources such as cubes are have limitations with respect to output viewing (e.g., drill down, etc.). Furthermore, with respect to data mining work flows, database mining system users want to be able to better integrate scores (e.g., data mining predictive or cluster model outputs) with user defined metrics and rules within the same visual, interactive work environment. Segments are not directly and easily connected to cost, profit, and other business drivers. That is, the process of integrating modeling outputs with strategies is time consuming, manual, and error prone. Moreover, validation of segmentation strategies is time consuming and error prone. Finally, deploying data mining models and strategies is a difficult process typically involving time consuming manual programming steps.
A need therefore exists for improved data mining methods and systems. Accordingly, a solution that addresses, at least in part, the above and other shortcomings is desired.